Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

经鼻导管高流量吸氧与经鼻气道正压通气在重症毛细支气管炎呼吸支持中的应用价值比较

目的：比较经鼻导管高流量吸氧（HFNC）与经鼻气道正压通气（nCPAP）在重症毛细支气管炎呼吸支持中的应用价值，为临床治疗方案的选择提供参考。方法：选取2016年12月至2018年12月我院儿科收治的重症毛细支气管炎患儿90例，采用随机数字表法分为观察组和对照组各45例。两组患儿入院后均给予常规综合治疗以保证呼吸道通畅，在此基础上观察组采用HFNC治疗，对照组采用nCPAP治疗，比较两组患儿治疗前和治疗24 h后呼吸频率、经皮血氧饱和度（TcSO2）、呼吸窘迫评分体系（CSS）评分、动脉血氧分压（PaO2）等呼吸相关指标及治疗前后临床症状体征改善情况。结果：两组患儿治疗24 h后呼吸频率、CSS评分均降低，且观察组降低程度更大，TcSO2、PaO2于治疗24 h后升高，观察组升高幅度较对照组明显；治疗后两组患儿咳嗽及肺部湿啰音、肺部炎症情况均改善，观察组症状体征消失时间早于对照组，差异均有统计学意义（P<0.05）。结论：重症毛细支气管炎患儿采用HFNC治疗可明显改善通气功能和临床症状，治疗效果优于nCPAP治疗，可扩大样本量进一步观察。     

Detrended fluctuation analysis detects altered coordination of running gait in athletes following a heavy period of training

Objectives

To investigate whether functional overreaching affects locomotor system behaviour when running at fixed relative intensities and if any effects were associated with changes in running performance.

Predictive factors of positive online patient ratings of spine surgeons

BACKGROUND

Online physician rating websites are increasingly used by patients to evaluate their doctors. The purpose of this investigation was to evaluate factors associated with better spine surgeon ratings.

不同规格怀牛膝不同极性部位HPLC指纹图谱

目的研究并比较不同规格怀牛膝不同极性部位的HPLC指纹图谱,探索其内部质量差异,为该药材的规格标准完善及临床用药提供参考。方法将怀牛膝用体积分数75%乙醇水浴回流提取,得体积分数75%乙醇回流提取物,用40 mL水溶解后,依次用石油醚、三氯甲烷、乙酸乙酯、正丁醇进行萃取,得到各萃取相及萃取后的水相,浓缩至浸膏,采集各部位HPLC指纹图谱;运用相似度、综合聚类法进行数据分析,同时对其不同部位的指纹图谱进行比对分析。结果石油醚、氯仿部位均标定了11个共有峰,乙酸乙酯部位标定了10个共有峰,正丁醇部位标定了19个共有峰,水部位标定8个共有峰;氯仿部位指纹图谱相似度差异较大,其他部位指纹图谱相似度差异较小,均在0.9以上;石油醚部位指纹图谱差异主要体现在峰高,乙酸乙酯部位、氯仿部位、水部位的化学成分种类及峰高均存在差异;综合聚类分析能将不同规格怀牛膝区分开。结论不同规格怀牛膝内部质量存在差异;实验中所建立的HPLC指纹图谱可以全面反映不同规格怀牛膝的化学成分分布,为不同规格怀牛膝的整体质量评价提供参考。